Research Article Landscape structure influences continental distribution of hantavirus in deer mice
Bibliographic record
Abstract
We hypothesized that landscape structure affects movement of individuals through the landscape, which affects the rate and pattern of disease transmission. Based on this hypothesis, we predicted a relationship between landscape structure and disease incidence in spatially structured populations. We tested this prediction for hantavirus inci-dence in deer mice (Penomysens moniculatus), using a novel index of habitat fragmentation for transect data. A series of four stepwise logistic regression analyses were conducted on serological and ecological data from 2837 mice from 101 sites across Canada. The significant variables, ranked in decreasing order of size of their effect on virus incidence were: human buildings, landscape composition (amount of deer mouse habitat in the 1-km radius landscape surrounding each site), landscape configuration (fragmentation of deer mouse habitat in the 1-km radius landscape surrounding each site), mean annual temperature, and seasonal variation. Our results suggest that epidemiological models should consider not only the demographic structure of the host population, but its spatial structure as well, as inferred from landscape structure. Landscape structure can have a greater effect on the pattern of distribution of a virus in its host population than other ecological variables such as climate and seasonal change. The usefulness of landscape data in epidemiological models depends on the use of the appropriate spatial scale, which can be determined empirically. Epidemiological models with a spatially structured host population
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".